The role of feature construction in inductive rule learning

Peter A. Flach and Nada Lavrac. In Luc De Raedt and Stefan Kramer, editors, Proceedings of the ICML2000 workshop on Attribute-Value and Relational Learning: crossing the boundaries, pages 1--11, Stanford, USA, July 2000. More behind this link.

Abstract

This paper proposes a unifying framework for inductive rule learning algorithms. We suggest that the problem of constructing an appropriate inductive hypothesis (set of rules) can be broken down in the following subtasks: rule construction, body construction, and feature construction. Each of these subtasks may have its own declarative bias, search strategies, and heuristics. In particular, we argue that feature construction is a crucial notion in explaining the relations between attribute-value rule learning and inductive logic programming (ILP). We demonstrate this by a general method for transforming ILP problems to attribute-value form, which overcomes some of the traditional limitations of propositionalisation approaches.

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P A Flach, Peter.Flach@bristol.ac.uk,